In light of the above issues, we develop a hyperedge-based graph neural network, namely HGNN, for CR. Specifically, (1) to model the relationships among learners, we treat learners (i.e., hyperedges) as the sets of courses in a hypergraph, and convert the task of learning learners' ...
【2019/ICML】DAG-GNN: DAG Structure Learning with Graph Neural Networks 原文链接:https://dreamhomes.github.io/posts/202101041501.html 文章链接:https://arxiv.org/abs/1904.10098 源码链接:https://github.com/fishmoon1234/DAG-GNN TL;DR 论文中提出一种新的DAG编码架构 DAG-GNN,其实模型的本质就是一...
Hypergraph-based neural networks for hyperedge prediction are usually categorized into two types during feature learning. In the first type, hyperedges are treated as fully connected subgraphs (i.e., where any pair of nodes is connected) that are projected into a simple graph (i.e., the edge...
Hypergraph neural networks have demonstrated outstanding performance in various fields. However, there is still a relative lack of research on the security aspects of hypergraph neural networks, particularly in terms of adversarial attack methods, when compared to graph neural networks. The existence of...
第一,基于 AI 的片段化方法:该研究基于图神经网络 (Graph Neural Network, GNN) 架构,采用 DigFrag 方法对分子进行片段化处理。 基于AI 的片段化方法 如上图 A 所示,研究人员将分子图 (molecular graph) 定义为 G=(V, E),其中 V 代表节点,对应于分子中的原子,而 E 代表连接边,对应于原子之间的化学键。
题目: HYPER-SAGNN: A Self-Attention Based Graph Neural Network for Hypergraphs 作者: Ruochi Zhang, Yuesong Zou, Jian Ma Paper: Link (ICLR 2020) 机构: CMU, Tsinghua Code: Source code Summary: 本文提出了一种超边预测模型,其主体为基于self-attention的超图图神经网络模型,可以应对具有不同大小的超...
为此,来自中国人民大学高瓴人工智能学院的研究团队,近期在 AI 领域顶级学术会议 ICML 2024 上,发表了题为「EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction」的研究论文。该研究首次将 E(3) 等变图神经网络 (GNN) 应用于配体结合位点预测,提出名为 EquiPocket...
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs,ICLR(2020)Ruochi Zhang,Yuesong Zou,Jian Ma 这篇文章针对的是hypergraph,并且使用的数据是scHi-C(single cell Hi-C),两种感觉都不是经常能见到的名词凑一起了。 本文处理的对象是Hi-C数据集,但在实验里也给出了其他可供参考的数据...
The input to a Graph Neural Network (GNN) is a molecular graph, where nodes represent atoms, edges represent bonds, and both nodes and edges have associated features. Within Massage Passing stage, nodes exchange information with their neighbors, aggregate this information, and update their features...
2021. Distributed hybrid CPU and GPU training for graph neural networks on billion-scale graphs. Retrieved from arxiv.org/abs/2112.1534. [194]Zhu Xiaojin and Ghahramani Zoubin. 2002. Learning from labeled and unlabeled data with label propagation. Technical Report....